PatientProfiler is an R package that allows personalized analysis of an entire cohort and specific individuals. The main functions can be used sequentially or separately.

If we want to imagine a sequential way to use PatientProfiler, we can divide the pipeline into 4 main steps:

In this vignette, we describe how to access pre-harmonized CPTAC data to use them in PatientProfiler pipeline.

Load necessary libraries

library(PatientProfiler)
library(SignalingProfiler)
library(tidyverse)

ACCESS TO PRE-HARMONIZED CPTAC DATA

access_harmonized_CPTAC_data

If you want to use pre-harmonized data for running PatientProfiler, you can access them using the following function. You can specify one or a set of tumor types. start with this function of PatientProfiler. Our processed database includes transcritpomics, proteomics and phosphoproteomics data for 10 different tumors:

  • Brca - Breast cancer

  • Ccrcc - Clear cell renal cell carcinoma

  • Coad - Colon adenocarcinoma

  • Gbm - Glioblastoma

  • Hnscc -Head and neck squamous cell cacinoma

  • Lscc - Lung squamous cell carcinoma

  • Luad - Lung cancer

  • Ov - Ovarian cancer

  • Pdac - Pancreatic ductal adenocarcinoma

  • Ucec - Uterine corpus endometrial carcinoma

All the data has been harmonized using [omics_update] function. The function returns an RDS file containing CPTAC pre-harmonized data for user-selected tumor types.

Example of usage:

If you need to extract Brca and Ov phosphoproteomics and proteomics data:

access_harmonized_CPTAC_data(tumors = c("Brca", "Ov"), omics = c("phospho", "prot"))

The output is a local variable for each data frame you acceded.

NEXT STEPS IN PatientProfiler

You can follow the main vignette to use the CPTAC pre-harmonized data and generate patient-specific networks.